JOURNAL PAPER

Md. Shahadad Hossain, Nobonita Shobnom, Umme Sumaiya Shams, Rezaul Roni, Evaluation of Spatio-Temporal Forest Health of Bangladesh Using Google Earth Engine, Iraqi National Journal of Earth Science, 26, 1, pp.193-207, 2026. doi: https://doi.org/10.33899/injes.v26i1.60201

Bangladesh has a significantly low and steadily declining land covered in forest compared to the required one-third. Therefore, it is essential to explore the condition of the forest cover in Bangladesh. In recent years, remote sensing techniques have gained significant popularity for assessing the health of forests. The research evaluates the forest health seasonality and spatiotemporal variability. Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 images for 2002-2021 and seven vegetation indices are used in the Google Earth Engine platform as it is widely accepted and convenient. The results reveal the time series analysis of vegetation indices; they show a maximum value of 0.8107 for SAVI in Sundarban and a minimum value of 0.0146 for NDVI in the Dinajpur and Hill Tract areas. Also, spatial variability illustrated a maximum value of 0.8107 for SAVI in Sundarban and a minimum value of 0.0146 for NDVI in the Dinajpur area. Moreover, Seasonal patterns are also identified where forest health is best observed during the monsoon season (July - October). Furthermore, the assessment indicates that the south and southeastern portions of the research region, Sundarban, and the Hill Tract area have healthier forest cover than the others. This study could be considered a comprehensive reference for managing and planning forests.

Shobnom N, Hossain MS, Roni, R, Monitoring spatiotemporal changes of NO2 using TROPOMI and sentinel-5 images for Dhaka city and its surrounding areas of Bangladesh, Journal of Air Pollution and Health, 8, 3, 2023. doi: https://doi.org/10.18502/japh.v8i3.13785

Abstract: Discernable air pollution occurs in most developing countries due to rapid urbanization which can be parameterized by air, humidity, population density, temperature, contaminants, exorbitant fossil fuel consumption, and inadequate transportation. Nitrogen dioxide (NO2), one of the most widely recognized air pollutants, has a detrimental impact on human health explicitly or implicitly and considerably influences on atmospheric composition. In this study, NO2 intensity was analyzed from 2018 aiming to monitor spatiotemporal changes in Dhaka and its surrounding areas with the Tropospheric Monitoring Instrument (TROPOMI) sensor data. Copernicus Sentinel-5 Precursor satellite data was used in the Google Earth Engine platform to get the result. The results revealed a strong relationship (R2=0.9478) between the NO2 concentration and high population density and the temporal variation is higher during the pre-monsoon than throughout the post-monsoon. The reason behind this is the lack of sunlight and the difficulty in breaking down the NO2, which causes the removal of NO2 from the atmosphere to proceed more slowly. In contrast, Land Use and Land Cover (LULC) are also impacted by the high concentration that remains in the built-up area. This research mainly considered how NO2 concentration was measured from satellite images with temporal variation within a year and what factors strongly influence raising NO2 levels. This model can be used for policy-making to take proper initiatives to reduce NO2 concentrations. The result showed significant uses of TROPOMI with relating population density and LULC in Dhaka and its surrounding areas of Bangladesh.

Roni, R. (2020), Extracting Building from Very High-Resolution Satellite Image through Object-based Image Analysis for Dhaka City, The Jahangirnagar Review, Part-II: Social Sciences, XLII, 2018, doi: ISSN 1682-7422
Roni, R.; Jia, P, An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh, 12, 7, 2020. doi: https://doi.org/10.3390/rs12071184

Traditional choropleth maps, created on the basis of administrative units, often fail to accurately represent population distribution due to the high spatial heterogeneity and the temporal dynamics of the population within the units. Furthermore, updating the data of spatial population statistics is time-consuming and costly, which underlies the relative lack of high-resolution and high-quality population data for implementing or validating population modeling work, in particular in low- and middle-income countries (LMIC). Dasymetric modeling has become an important technique to produce high-resolution gridded population surfaces. In this study, carried out in Dhaka City, Bangladesh, dasymetric mapping was implemented with the assistance of a combination of an object-based image analysis method (for generating ancillary data) and Geographically Weighted Regression (for improving the accuracy of the dasymetric modeling on the basis of building use). Buildings were extracted from WorldView 2 imagery as ancillary data, and a building-based GWR model was selected as the final model to disaggregate population counts from administrative units onto 5 m raster cells. The overall accuracy of the image classification was 77.75%, but the root means square error (RMSE) of the building-based GWR model for the population disaggregation was significantly less compared to the RMSE values of GWR based land use, Ordinary Least Square based land use and building modeling. Our model has the potential to be adapted to other LMIC countries, where high-quality ground-truth population data are lacking. With increasingly available satellite data, the approach developed in this study can facilitate high-resolution population modeling in a complex urban setting, and hence improve the demographic, social, environmental and health research in LMICs.

Keywords: population; geographically weighted regression; GWR; dasymetric mapping; remote sensing; satellite image

Requirements and Importance for Standardization of Place Names of Bangladesh: A study on Meherpur District,

Roni, R & S. Dara Shamsuddin (2019). Requirements and Importance for Standardization of Place Names of Bangladesh: A study on Meherpur District. Jahangirnagar Bishwavidyalay Bhugol O Paribesh Samikkhan. Vol 37. Jahangirnagar University. ISSN 1027-8567.

Methodology for Development of GIS Database for Master Plan,

Roni, R. (2011). Methodology for development of GIS database for master plan. Social Science Review, p: 145-155, Jahangirnagar University. ISSN 1682-422.

Surface Temperature and NDVI Generation and Relation between Them: Application of Remote Sensing,

Roni, R. (2013). Surface temperature and NDVI generation and relation between them. Application of Remote Sensing, Asian Journal of Engineering and Technology Innovation 01 (01), p: 08-13. ISSN: 2347-7385.

Seasonality and Food Insecurity: A Study on Sundarban Impact Zone of Bangladesh,

Roni, R. & Iqbal, M. (2013). Seasonality and food insecurity: a study on Sundarban impact zone of Bangladesh. Environmental Science Journal, Jahangirnagar University.

Sundarban Reserve Forest Resource Collection Process: A Study on Golpata, Fish-shrimp and Mud-crab,

Roni, R. & Islam, S. T. (2015). Sundarban reserve forest resource collection process: a study on golpata, fish-shrimp and mud-crab. The Jahangirnagar Review, Part II: Social Science, Vol:XXXV. Printed in June 2015, p-145-156.

Assessing Socio-Demographic Vulnerability of Dhaka City Corporation.,

Mary, M. S., Kamal, A. & Roni, R., (2015). Assessing Socio-Demographic Vulnerability of Dahaka City Corporation. The Jahangirnagar Review, Part II: Social Science, Vol:XXXVI. Printed in June 2015, p-79-94

Rainfall modelling of coastal areas in Bangladesh: extreme-value approach,

Sultana, N. & Roni, R. (2015). Rainfall modelling of coastal areas in Bangladesh: extreme-value approach. Journal of Science, Technology & Environment Informatics, 02(02), 42–50. DOI: http://dx.doi.org/10.18801/jstei.020215.15

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BOOK

Climate Change: Issues and Perspectives of South Asia,

SMALL AREA ATLAS OF BANGLADESH,

Small Area Atlas of Bangladesh (2015), Bangladesh Bureau of Statistics. ISBN-978-984-33-8553-6.

Climate Variability: Issues and Perspectives for Bangladesh.,

Shamsuddin, S. D., Ahmed, R & Roni, R (2015). Introduction to some basic concepts of weather and climate. In Shamsuddin, S. D., Ahmed, R & Jahan, R (Eds.), Climate Variability: Issues and Perspectives for Bangladesh, pp. 3-20, Shahitya Prakash, Dhaka, Bangladesh, ISBN: 984-70124-0218-4.

Variability of Temperature at 12 Selected Stations in Bangladesh Between 1948 - 1979 and 1980 to 2011,

Jahan, R, Roni, R & Shamsuddin, S. D. (2015). Variability of temperature at 12 selected stations in Bangladesh between 1948 - 1979 and 1980 to 2011. In Shamsuddin, S. D., Ahmed, R & Jahan, R (Eds.), Climate Variability: Issues and Perspectives for Bangladesh, pp. 53-69,  Shahitya Prakash, Dhaka, Bangladesh, ISBN: 984-70124-0218-4.

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BOOK CHAPTER

Rezaul Roni, A Review on Population Modeling: Statistical and Remote Sensing Perspective, Contemporary Issues in Social Sciences, pp.143-170, Faculty of Social Science, Jahangirnagar University, 2022.
Rezaul Roni, Shah Nurul Hasnat Sadi and Abdullah Al Mamun, Quantifying Aboveground Carbon Stock at Species Level Using TLS LiDAR and UAV Photogrammetry for Urban Trees, pp.138, 2025. doi: DOI: 10.5772/intechopen.1009808

Urbanization is increasing the depletion of natural carbon sinks and the intensification of urban heat islands, creating urban vegetation critical for carbon sequestration and climate regulation. In this study, a fusion approach was applied that combined Terrestrial Laser Scanning (TLS) Light Detection and Ranging (LiDAR) with high-resolution Unmanned Aerial Vehicle (UAV) imagery to estimate the aboveground carbon stock of individual trees along Manik Mia Avenue, Dhaka, Bangladesh. UAV imageries and dense point cloud data from TLS LiDAR were collected and georeferenced using Real-Time Kinematic (RTK) GPS. After screening and contouring the models to filter the aboveground vegetation, it was possible to segment individual trees, measure tree height and diameter at breast height (DBH), and calculate aboveground carbon stock through species-specific allometric equations. The results indicate a strong correlation between field-measured and point cloud-derived height (r2 = 0.94, RMSE = 0.49) and DBH (r2 = 0.88). While species-specific carbon stock estimation achieved a high correlation (r2 = 0.80), species with aerial roots posed challenges in DBH measurement, resulting in a low correlation (r2 = 0.26) when assessed separately. Limitations include insufficient scanning angles in TLS, variability in point cloud density, and constraints of non-invasive techniques. Future research could integrate multispectral data and geometric shape fitting to address species-specific challenges and enhance precision, contributing to urban carbon management and Sustainable Development Goals (SDGs) 11 and 15, which are related to sustainable cities and forest management.

Toma, R.A., Rabby, M.F., Roni, R., Rashid, M.S., Assessing the Efficiency of Classification Techniques Between SVM and ML for Detecting Land Transformation in Bhawal Sal Forest, Springer, pp.443–458, 2022. doi: https://doi.org/10.1007/978-3-030-77572-8_23

Abstract: Globally forests are endangered through deforestation and degradation where human or climate change is playing a vital role, and Bangladesh is not out of the context. Bangladesh is facing substantial deforestation since the last few decades. Geographic information system (GIS) and remote sensing (RS) is used to detect the changes, where satellite images are freely available and collecting data from the field is costly and time consuming for developing countries like Bangladesh. Maximum likelihood (ML) and support vector machine (SVM) are the most effective and easy-to-use algorithms in remote sensing. This research reveals the suitability of land cover classification techniques between ML and SVM for Landsat 5 and 8 with five types of land covers, for example, dense vegetation, light vegetation, water, built-up, and bare land, where forest was classified as vegetation. The error matrix was used to compare the result, and the accuracy of SVM was found to be higher (98.04%) than ML (93.44%). The kappa coefficient returned with values of 0.89 and 0.97 for ML and SVM, respectively. Afterward, the SVM method was applied to detect the forest cover change over the two decades and found that 0.37% Bhawal Sal Forest land transformed to built-up area and 15.42% land transformed into bare soil, which indicates the national deforestation status. The transformation contributes to an increase in localized natural disasters such as floods, top soil erosion, and ecosystem habitat. Further studies could be conducted with high-resolution satellite images and more field-based data that can be used to improve the accuracy of the land cover changes. The results may help to prepare the guideline for Sal forests’ protection and management planning efforts.

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WORKSHOP

Attended “Capacity Development and Consultation Workshop on Bangladesh National Data Governance Framework”, organized by a2i on 14-15 March 2023 at Hotel Sonargaon, Dhaka.

2023.

Training program on “Fundamentals of GIS and Remote sensing” organized by the Department of Geography and Environment, Jahangirnagar University from 04-08 June 2023 for the participants of National Agricultural Training Academy (NATA), Gazipur.

Training workshop on “Roadmap to implement the System of Environmental-Economic Accounting (SEEA) including Blue Economy and Poverty-Environment Nexus (PEN) in Bangladesh” in collaboration with UNDP Bangladesh in December 7-8, 2022.

Workshop on “Preparing Action Plan for Sub-committee 5 and 14 to Implement Smart Bangladesh 2041” organized by ICT division, GoB from December 19-21, 2023.

Training workshop on “SDG Metadata and SDMX Template: Exercise on SDG Indicator 11.3.1” organized by The General Economics Division (GED) of the Bangladesh Planning Commission, with the support of UNDP and UNDP-UNEP Poverty-Environment Action on 14-15 November 2022.

November 2022.

Participated as a Resource Person in a day-long workshop on Geospatial data standardization in Bangladesh in the Bangladesh Bureau of Statistics funded by UNFPA

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